Overview

Dataset statistics

Number of variables21
Number of observations344878
Missing cells142524
Missing cells (%)2.0%
Duplicate rows122
Duplicate rows (%)< 0.1%
Total size in memory53.0 MiB
Average record size in memory161.0 B

Variable types

Boolean2
Numeric16
Categorical3

Alerts

Dataset has 122 (< 0.1%) duplicate rowsDuplicates
LanguageCode is highly overall correlated with Gender and 1 other fieldsHigh correlation
UseOfLoan is highly overall correlated with MaritalStatus and 3 other fieldsHigh correlation
MaritalStatus is highly overall correlated with UseOfLoan and 2 other fieldsHigh correlation
EmploymentStatus is highly overall correlated with UseOfLoan and 2 other fieldsHigh correlation
OccupationArea is highly overall correlated with UseOfLoan and 2 other fieldsHigh correlation
ExistingLiabilities is highly overall correlated with LiabilitiesTotal and 1 other fieldsHigh correlation
LiabilitiesTotal is highly overall correlated with ExistingLiabilitiesHigh correlation
NoOfPreviousLoansBeforeLoan is highly overall correlated with ExistingLiabilities and 2 other fieldsHigh correlation
AmountOfPreviousLoansBeforeLoan is highly overall correlated with NoOfPreviousLoansBeforeLoan and 1 other fieldsHigh correlation
PreviousRepaymentsBeforeLoan is highly overall correlated with NoOfPreviousLoansBeforeLoan and 1 other fieldsHigh correlation
Gender is highly overall correlated with LanguageCode and 1 other fieldsHigh correlation
Country is highly overall correlated with LanguageCode and 1 other fieldsHigh correlation
EmploymentDurationCurrentEmployer is highly overall correlated with UseOfLoanHigh correlation
EmploymentDurationCurrentEmployer has 8639 (2.5%) missing valuesMissing
PreviousRepaymentsBeforeLoan has 131766 (38.2%) missing valuesMissing
IncomeTotal is highly skewed (γ1 = 63.91325769)Skewed
LiabilitiesTotal is highly skewed (γ1 = 583.7806183)Skewed
UseOfLoan has 6914 (2.0%) zerosZeros
ExistingLiabilities has 71958 (20.9%) zerosZeros
LiabilitiesTotal has 74797 (21.7%) zerosZeros
NoOfPreviousLoansBeforeLoan has 152481 (44.2%) zerosZeros
AmountOfPreviousLoansBeforeLoan has 152475 (44.2%) zerosZeros
PreviousRepaymentsBeforeLoan has 30663 (8.9%) zerosZeros

Reproduction

Analysis started2023-12-10 11:08:20.853614
Analysis finished2023-12-10 11:10:32.553862
Duration2 minutes and 11.7 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size336.9 KiB
True
184798 
False
160080 
ValueCountFrequency (%)
True 184798
53.6%
False 160080
46.4%
2023-12-10T13:10:34.047854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

ApplicationSignedWeekday
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9696037
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:34.245777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7978521
Coefficient of variation (CV)0.45290468
Kurtosis-1.0916083
Mean3.9696037
Median Absolute Deviation (MAD)2
Skewness0.042800901
Sum1369029
Variance3.2322722
MonotonicityNot monotonic
2023-12-10T13:10:34.373424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 61463
17.8%
2 57392
16.6%
5 56285
16.3%
4 54607
15.8%
6 53688
15.6%
7 31072
9.0%
1 30371
8.8%
ValueCountFrequency (%)
1 30371
8.8%
2 57392
16.6%
3 61463
17.8%
4 54607
15.8%
5 56285
16.3%
6 53688
15.6%
7 31072
9.0%
ValueCountFrequency (%)
7 31072
9.0%
6 53688
15.6%
5 56285
16.3%
4 54607
15.8%
3 61463
17.8%
2 57392
16.6%
1 30371
8.8%

LanguageCode
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1655542
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:34.622540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q34
95-th percentile6
Maximum22
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5203156
Coefficient of variation (CV)0.79616884
Kurtosis19.571625
Mean3.1655542
Median Absolute Deviation (MAD)1
Skewness3.4499632
Sum1091730
Variance6.3519908
MonotonicityNot monotonic
2023-12-10T13:10:34.852033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 141727
41.1%
1 127073
36.8%
3 36135
 
10.5%
6 30977
 
9.0%
19 4897
 
1.4%
2 3753
 
1.1%
9 296
 
0.1%
22 7
 
< 0.1%
5 5
 
< 0.1%
8 3
 
< 0.1%
Other values (5) 5
 
< 0.1%
ValueCountFrequency (%)
1 127073
36.8%
2 3753
 
1.1%
3 36135
 
10.5%
4 141727
41.1%
5 5
 
< 0.1%
6 30977
 
9.0%
7 1
 
< 0.1%
8 3
 
< 0.1%
9 296
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
22 7
 
< 0.1%
21 1
 
< 0.1%
19 4897
 
1.4%
15 1
 
< 0.1%
13 1
 
< 0.1%
10 1
 
< 0.1%
9 296
 
0.1%
8 3
 
< 0.1%
7 1
 
< 0.1%
6 30977
9.0%

Age
Real number (ℝ)

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.402102
Minimum0
Maximum77
Zeros48
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:34.981749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q131
median39
Q349
95-th percentile63
Maximum77
Range77
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.21077
Coefficient of variation (CV)0.30223106
Kurtosis-0.67930022
Mean40.402102
Median Absolute Deviation (MAD)9
Skewness0.36545022
Sum13933796
Variance149.1029
MonotonicityNot monotonic
2023-12-10T13:10:35.211036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 10773
 
3.1%
32 10685
 
3.1%
34 10685
 
3.1%
35 10617
 
3.1%
31 10538
 
3.1%
38 10471
 
3.0%
37 10454
 
3.0%
30 10396
 
3.0%
36 10227
 
3.0%
39 9838
 
2.9%
Other values (52) 240194
69.6%
ValueCountFrequency (%)
0 48
 
< 0.1%
1 2
 
< 0.1%
2 3
 
< 0.1%
18 1099
 
0.3%
19 1549
 
0.4%
20 2274
 
0.7%
21 5778
1.7%
22 6379
1.8%
23 6554
1.9%
24 6811
2.0%
ValueCountFrequency (%)
77 3
 
< 0.1%
76 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
72 3
 
< 0.1%
71 2
 
< 0.1%
70 1245
0.4%
69 1591
0.5%
68 1829
0.5%
67 2064
0.6%

Gender
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing45
Missing (%)< 0.1%
Memory size2.6 MiB
0.0
194810 
1.0
133313 
2.0
 
16710

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1034499
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 194810
56.5%
1.0 133313
38.7%
2.0 16710
 
4.8%
(Missing) 45
 
< 0.1%

Length

2023-12-10T13:10:35.595250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T13:10:35.857139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 194810
56.5%
1.0 133313
38.7%
2.0 16710
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 539643
52.2%
. 344833
33.3%
1 133313
 
12.9%
2 16710
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 689666
66.7%
Other Punctuation 344833
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 539643
78.2%
1 133313
 
19.3%
2 16710
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 344833
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1034499
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 539643
52.2%
. 344833
33.3%
1 133313
 
12.9%
2 16710
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1034499
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 539643
52.2%
. 344833
33.3%
1 133313
 
12.9%
2 16710
 
1.6%

Country
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
EE
164671 
FI
143804 
ES
31111 
NL
 
4996
SK
 
296

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters689756
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEE
2nd rowFI
3rd rowEE
4th rowFI
5th rowEE

Common Values

ValueCountFrequency (%)
EE 164671
47.7%
FI 143804
41.7%
ES 31111
 
9.0%
NL 4996
 
1.4%
SK 296
 
0.1%

Length

2023-12-10T13:10:35.987434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T13:10:36.165195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
ee 164671
47.7%
fi 143804
41.7%
es 31111
 
9.0%
nl 4996
 
1.4%
sk 296
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 360453
52.3%
F 143804
 
20.8%
I 143804
 
20.8%
S 31407
 
4.6%
N 4996
 
0.7%
L 4996
 
0.7%
K 296
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 689756
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 360453
52.3%
F 143804
 
20.8%
I 143804
 
20.8%
S 31407
 
4.6%
N 4996
 
0.7%
L 4996
 
0.7%
K 296
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 689756
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 360453
52.3%
F 143804
 
20.8%
I 143804
 
20.8%
S 31407
 
4.6%
N 4996
 
0.7%
L 4996
 
0.7%
K 296
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 689756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 360453
52.3%
F 143804
 
20.8%
I 143804
 
20.8%
S 31407
 
4.6%
N 4996
 
0.7%
L 4996
 
0.7%
K 296
 
< 0.1%

AppliedAmount
Real number (ℝ)

Distinct2460
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2690.9917
Minimum10
Maximum15948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:36.436491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile518
Q1850
median2126
Q34146
95-th percentile6911
Maximum15948
Range15938
Interquartile range (IQR)3296

Descriptive statistics

Standard deviation2145.4524
Coefficient of variation (CV)0.79727201
Kurtosis2.2005165
Mean2690.9917
Median Absolute Deviation (MAD)1595
Skewness1.3225527
Sum9.2806383 × 108
Variance4602965.8
MonotonicityNot monotonic
2023-12-10T13:10:36.633756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4150 27738
 
8.0%
530 20389
 
5.9%
4146 20053
 
5.8%
531 19456
 
5.6%
518 15991
 
4.6%
2126 13399
 
3.9%
2125 9312
 
2.7%
4253 7028
 
2.0%
4250 6569
 
1.9%
1063 6535
 
1.9%
Other values (2450) 198408
57.5%
ValueCountFrequency (%)
10 1
 
< 0.1%
31.9558 29
< 0.1%
38.347 6
 
< 0.1%
44.7382 10
 
< 0.1%
51.1293 17
 
< 0.1%
57.5205 11
 
< 0.1%
63.9116 59
< 0.1%
70.3028 9
 
< 0.1%
76.694 15
 
< 0.1%
83.0851 10
 
< 0.1%
ValueCountFrequency (%)
15948 2
< 0.1%
12994 1
< 0.1%
12971 1
< 0.1%
12385 1
< 0.1%
12333 1
< 0.1%
12266 1
< 0.1%
12173 1
< 0.1%
12163 1
< 0.1%
12031 1
< 0.1%
11992 2
< 0.1%

UseOfLoan
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4760263
Minimum-1
Maximum110
Zeros6914
Zeros (%)2.0%
Negative308290
Negative (%)89.4%
Memory size2.6 MiB
2023-12-10T13:10:36.808589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile4
Maximum110
Range111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1698677
Coefficient of variation (CV)-4.5582937
Kurtosis869.90627
Mean-0.4760263
Median Absolute Deviation (MAD)0
Skewness19.579154
Sum-164171
Variance4.7083258
MonotonicityNot monotonic
2023-12-10T13:10:36.940915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
-1 308290
89.4%
7 9700
 
2.8%
2 9191
 
2.7%
0 6914
 
2.0%
6 3151
 
0.9%
3 1952
 
0.6%
5 1787
 
0.5%
8 1519
 
0.4%
4 1366
 
0.4%
1 955
 
0.3%
Other values (7) 53
 
< 0.1%
ValueCountFrequency (%)
-1 308290
89.4%
0 6914
 
2.0%
1 955
 
0.3%
2 9191
 
2.7%
3 1952
 
0.6%
4 1366
 
0.4%
5 1787
 
0.5%
6 3151
 
0.9%
7 9700
 
2.8%
8 1519
 
0.4%
ValueCountFrequency (%)
110 17
 
< 0.1%
108 1
 
< 0.1%
107 2
 
< 0.1%
106 1
 
< 0.1%
104 6
 
< 0.1%
102 21
 
< 0.1%
101 5
 
< 0.1%
8 1519
 
0.4%
7 9700
2.8%
6 3151
 
0.9%

Education
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.4007853
Minimum-1
Maximum5
Zeros8
Zeros (%)< 0.1%
Negative8353
Negative (%)2.4%
Memory size2.6 MiB
2023-12-10T13:10:37.067696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median4
Q34
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.391508
Coefficient of variation (CV)0.40917256
Kurtosis0.9130464
Mean3.4007853
Median Absolute Deviation (MAD)1
Skewness-1.0354739
Sum1172686
Variance1.9362946
MonotonicityNot monotonic
2023-12-10T13:10:37.241802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 107264
31.1%
4 102708
29.8%
5 78869
22.9%
1 41182
 
11.9%
-1 8353
 
2.4%
2 6444
 
1.9%
0 8
 
< 0.1%
(Missing) 50
 
< 0.1%
ValueCountFrequency (%)
-1 8353
 
2.4%
0 8
 
< 0.1%
1 41182
 
11.9%
2 6444
 
1.9%
3 107264
31.1%
4 102708
29.8%
5 78869
22.9%
ValueCountFrequency (%)
5 78869
22.9%
4 102708
29.8%
3 107264
31.1%
2 6444
 
1.9%
1 41182
 
11.9%
0 8
 
< 0.1%
-1 8353
 
2.4%

MaritalStatus
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing50
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.65257172
Minimum-1
Maximum5
Zeros8
Zeros (%)< 0.1%
Negative308290
Negative (%)89.4%
Memory size2.6 MiB
2023-12-10T13:10:37.354514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile2
Maximum5
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0641046
Coefficient of variation (CV)-1.6306325
Kurtosis8.2031437
Mean-0.65257172
Median Absolute Deviation (MAD)0
Skewness3.0421078
Sum-225025
Variance1.1323187
MonotonicityNot monotonic
2023-12-10T13:10:37.459112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-1 308290
89.4%
3 12400
 
3.6%
1 10752
 
3.1%
2 9400
 
2.7%
4 3377
 
1.0%
5 601
 
0.2%
0 8
 
< 0.1%
(Missing) 50
 
< 0.1%
ValueCountFrequency (%)
-1 308290
89.4%
0 8
 
< 0.1%
1 10752
 
3.1%
2 9400
 
2.7%
3 12400
 
3.6%
4 3377
 
1.0%
5 601
 
0.2%
ValueCountFrequency (%)
5 601
 
0.2%
4 3377
 
1.0%
3 12400
 
3.6%
2 9400
 
2.7%
1 10752
 
3.1%
0 8
 
< 0.1%
-1 308290
89.4%

EmploymentStatus
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing202
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-0.55035744
Minimum-1
Maximum6
Zeros32
Zeros (%)< 0.1%
Negative308290
Negative (%)89.4%
Memory size2.6 MiB
2023-12-10T13:10:37.587404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile3
Maximum6
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.335761
Coefficient of variation (CV)-2.4270791
Kurtosis6.6748452
Mean-0.55035744
Median Absolute Deviation (MAD)0
Skewness2.810459
Sum-189695
Variance1.7842575
MonotonicityNot monotonic
2023-12-10T13:10:37.704932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-1 308290
89.4%
3 30060
 
8.7%
5 2007
 
0.6%
6 1800
 
0.5%
4 1303
 
0.4%
2 1184
 
0.3%
0 32
 
< 0.1%
(Missing) 202
 
0.1%
ValueCountFrequency (%)
-1 308290
89.4%
0 32
 
< 0.1%
2 1184
 
0.3%
3 30060
 
8.7%
4 1303
 
0.4%
5 2007
 
0.6%
6 1800
 
0.5%
ValueCountFrequency (%)
6 1800
 
0.5%
5 2007
 
0.6%
4 1303
 
0.4%
3 30060
 
8.7%
2 1184
 
0.3%
0 32
 
< 0.1%
-1 308290
89.4%

EmploymentDurationCurrentEmployer
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)< 0.1%
Missing8639
Missing (%)2.5%
Memory size2.6 MiB
MoreThan5Years
125529 
UpTo5Years
89646 
UpTo1Year
62373 
Retiree
21325 
Other
20975 
Other values (4)
16391 

Length

Max length14
Median length11
Mean length10.807893
Min length5

Characters and Unicode

Total characters3634035
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUpTo5Years
2nd rowUpTo5Years
3rd rowMoreThan5Years
4th rowOther
5th rowUpTo5Years

Common Values

ValueCountFrequency (%)
MoreThan5Years 125529
36.4%
UpTo5Years 89646
26.0%
UpTo1Year 62373
18.1%
Retiree 21325
 
6.2%
Other 20975
 
6.1%
UpTo2Years 6504
 
1.9%
UpTo3Years 5445
 
1.6%
UpTo4Years 3690
 
1.1%
TrialPeriod 752
 
0.2%
(Missing) 8639
 
2.5%

Length

2023-12-10T13:10:37.867871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T13:10:38.047810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
morethan5years 125529
37.3%
upto5years 89646
26.7%
upto1year 62373
18.6%
retiree 21325
 
6.3%
other 20975
 
6.2%
upto2years 6504
 
1.9%
upto3years 5445
 
1.6%
upto4years 3690
 
1.1%
trialperiod 752
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 504418
13.9%
r 462520
12.7%
a 419468
11.5%
T 293939
8.1%
o 293939
8.1%
Y 293187
8.1%
s 230814
 
6.4%
5 215175
 
5.9%
p 167658
 
4.6%
U 167658
 
4.6%
Other values (14) 585259
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2417483
66.5%
Uppercase Letter 923365
 
25.4%
Decimal Number 293187
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 504418
20.9%
r 462520
19.1%
a 419468
17.4%
o 293939
12.2%
s 230814
9.5%
p 167658
 
6.9%
h 146504
 
6.1%
n 125529
 
5.2%
t 42300
 
1.7%
i 22829
 
0.9%
Other values (2) 1504
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
T 293939
31.8%
Y 293187
31.8%
U 167658
18.2%
M 125529
13.6%
R 21325
 
2.3%
O 20975
 
2.3%
P 752
 
0.1%
Decimal Number
ValueCountFrequency (%)
5 215175
73.4%
1 62373
 
21.3%
2 6504
 
2.2%
3 5445
 
1.9%
4 3690
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3340848
91.9%
Common 293187
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 504418
15.1%
r 462520
13.8%
a 419468
12.6%
T 293939
8.8%
o 293939
8.8%
Y 293187
8.8%
s 230814
6.9%
p 167658
 
5.0%
U 167658
 
5.0%
h 146504
 
4.4%
Other values (9) 360743
10.8%
Common
ValueCountFrequency (%)
5 215175
73.4%
1 62373
 
21.3%
2 6504
 
2.2%
3 5445
 
1.9%
4 3690
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3634035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 504418
13.9%
r 462520
12.7%
a 419468
11.5%
T 293939
8.1%
o 293939
8.1%
Y 293187
8.1%
s 230814
 
6.4%
5 215175
 
5.9%
p 167658
 
4.6%
U 167658
 
4.6%
Other values (14) 585259
16.1%

OccupationArea
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)< 0.1%
Missing91
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-0.071299092
Minimum-1
Maximum19
Zeros11
Zeros (%)< 0.1%
Negative308341
Negative (%)89.4%
Memory size2.6 MiB
2023-12-10T13:10:38.216152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile7
Maximum19
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2612322
Coefficient of variation (CV)-45.740165
Kurtosis15.5485
Mean-0.071299092
Median Absolute Deviation (MAD)0
Skewness3.9523966
Sum-24583
Variance10.635636
MonotonicityNot monotonic
2023-12-10T13:10:38.429943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-1 308341
89.4%
1 8421
 
2.4%
7 3587
 
1.0%
6 3312
 
1.0%
3 3198
 
0.9%
8 2462
 
0.7%
17 2424
 
0.7%
9 2262
 
0.7%
10 1933
 
0.6%
15 1684
 
0.5%
Other values (11) 7163
 
2.1%
ValueCountFrequency (%)
-1 308341
89.4%
0 11
 
< 0.1%
1 8421
 
2.4%
2 122
 
< 0.1%
3 3198
 
0.9%
4 587
 
0.2%
5 362
 
0.1%
6 3312
 
1.0%
7 3587
 
1.0%
8 2462
 
0.7%
ValueCountFrequency (%)
19 1000
0.3%
18 619
 
0.2%
17 2424
0.7%
16 1430
0.4%
15 1684
0.5%
14 843
 
0.2%
13 564
 
0.2%
12 477
 
0.1%
11 1148
0.3%
10 1933
0.6%

HomeOwnershipType
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing1657
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean3.1939771
Minimum-1
Maximum10
Zeros186
Zeros (%)0.1%
Negative3
Negative (%)< 0.1%
Memory size2.6 MiB
2023-12-10T13:10:38.556924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median3
Q33
95-th percentile10
Maximum10
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8107968
Coefficient of variation (CV)0.88003036
Kurtosis0.71480621
Mean3.1939771
Median Absolute Deviation (MAD)2
Skewness1.4201177
Sum1096240
Variance7.9005786
MonotonicityNot monotonic
2023-12-10T13:10:38.656886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 128989
37.4%
3 105505
30.6%
2 40206
 
11.7%
10 28122
 
8.2%
8 27122
 
7.9%
4 4600
 
1.3%
7 3346
 
1.0%
5 2778
 
0.8%
6 1619
 
0.5%
9 745
 
0.2%
Other values (2) 189
 
0.1%
(Missing) 1657
 
0.5%
ValueCountFrequency (%)
-1 3
 
< 0.1%
0 186
 
0.1%
1 128989
37.4%
2 40206
 
11.7%
3 105505
30.6%
4 4600
 
1.3%
5 2778
 
0.8%
6 1619
 
0.5%
7 3346
 
1.0%
8 27122
 
7.9%
ValueCountFrequency (%)
10 28122
 
8.2%
9 745
 
0.2%
8 27122
 
7.9%
7 3346
 
1.0%
6 1619
 
0.5%
5 2778
 
0.8%
4 4600
 
1.3%
3 105505
30.6%
2 40206
 
11.7%
1 128989
37.4%

IncomeTotal
Real number (ℝ)

SKEWED 

Distinct6784
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2117.087
Minimum0
Maximum1012019
Zeros265
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:38.795476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile580
Q11000
median1532.21
Q32300
95-th percentile3850
Maximum1012019
Range1012019
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation10098.919
Coefficient of variation (CV)4.7701957
Kurtosis4686.4399
Mean2117.087
Median Absolute Deviation (MAD)592.21
Skewness63.913258
Sum7.3013671 × 108
Variance1.0198817 × 108
MonotonicityNot monotonic
2023-12-10T13:10:38.950995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 15161
 
4.4%
2000 13373
 
3.9%
1000 13262
 
3.8%
1500 12978
 
3.8%
2500 11287
 
3.3%
1300 9329
 
2.7%
1800 8890
 
2.6%
1100 8744
 
2.5%
2200 8180
 
2.4%
3000 8168
 
2.4%
Other values (6774) 235506
68.3%
ValueCountFrequency (%)
0 265
0.1%
0.1 4
 
< 0.1%
0.12 9
 
< 0.1%
0.13 2
 
< 0.1%
0.14 2
 
< 0.1%
0.15 2
 
< 0.1%
0.16 2
 
< 0.1%
0.17 7
 
< 0.1%
0.18 2
 
< 0.1%
0.2 5
 
< 0.1%
ValueCountFrequency (%)
1012019 2
 
< 0.1%
950600 1
 
< 0.1%
900555 1
 
< 0.1%
900000 1
 
< 0.1%
850950 1
 
< 0.1%
800900 15
< 0.1%
800850 1
 
< 0.1%
750800 1
 
< 0.1%
720600 1
 
< 0.1%
700800 6
 
< 0.1%

ExistingLiabilities
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5657508
Minimum0
Maximum40
Zeros71958
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:39.086000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8590634
Coefficient of variation (CV)1.1143184
Kurtosis8.6029431
Mean2.5657508
Median Absolute Deviation (MAD)1
Skewness2.305099
Sum884871
Variance8.1742434
MonotonicityNot monotonic
2023-12-10T13:10:39.223840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 86392
25.1%
0 71958
20.9%
2 58792
17.0%
3 40419
11.7%
4 27122
 
7.9%
5 18247
 
5.3%
6 12367
 
3.6%
7 8444
 
2.4%
8 5853
 
1.7%
9 4178
 
1.2%
Other values (29) 11106
 
3.2%
ValueCountFrequency (%)
0 71958
20.9%
1 86392
25.1%
2 58792
17.0%
3 40419
11.7%
4 27122
 
7.9%
5 18247
 
5.3%
6 12367
 
3.6%
7 8444
 
2.4%
8 5853
 
1.7%
9 4178
 
1.2%
ValueCountFrequency (%)
40 1
 
< 0.1%
39 2
 
< 0.1%
36 2
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 3
 
< 0.1%
32 7
< 0.1%
31 10
< 0.1%
30 8
< 0.1%
29 8
< 0.1%

LiabilitiesTotal
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct81277
Distinct (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean409.3809
Minimum0
Maximum12400000
Zeros74797
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:39.397149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143.1025
median237.85
Q3500
95-th percentile1200
Maximum12400000
Range12400000
Interquartile range (IQR)456.8975

Descriptive statistics

Standard deviation21156.541
Coefficient of variation (CV)51.679355
Kurtosis342129.6
Mean409.3809
Median Absolute Deviation (MAD)220.15
Skewness583.78062
Sum1.4118647 × 108
Variance4.4759923 × 108
MonotonicityNot monotonic
2023-12-10T13:10:39.589651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74797
 
21.7%
250 2878
 
0.8%
200 2712
 
0.8%
300 2666
 
0.8%
100 2450
 
0.7%
350 2178
 
0.6%
500 2059
 
0.6%
400 1968
 
0.6%
150 1937
 
0.6%
600 1395
 
0.4%
Other values (81267) 249838
72.4%
ValueCountFrequency (%)
0 74797
21.7%
0.01 1
 
< 0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.3 2
 
< 0.1%
0.42 1
 
< 0.1%
0.5 1
 
< 0.1%
0.51 1
 
< 0.1%
0.52 1
 
< 0.1%
0.55 1
 
< 0.1%
ValueCountFrequency (%)
12400000 1
< 0.1%
250151.29 1
< 0.1%
250125.81 1
< 0.1%
250100 1
< 0.1%
229000 1
< 0.1%
220116.23 1
< 0.1%
220000 1
< 0.1%
172510 1
< 0.1%
145041.53 1
< 0.1%
145021.15 1
< 0.1%

NoOfPreviousLoansBeforeLoan
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.7346912
Minimum0
Maximum74
Zeros152481
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:39.749457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum74
Range74
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7594303
Coefficient of variation (CV)1.5907329
Kurtosis31.438523
Mean1.7346912
Median Absolute Deviation (MAD)1
Skewness3.6633355
Sum598236
Variance7.6144558
MonotonicityNot monotonic
2023-12-10T13:10:39.893086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 152481
44.2%
1 70891
20.6%
2 41259
 
12.0%
3 25274
 
7.3%
4 16223
 
4.7%
5 10931
 
3.2%
6 7532
 
2.2%
7 5314
 
1.5%
8 3801
 
1.1%
9 2790
 
0.8%
Other values (65) 8370
 
2.4%
ValueCountFrequency (%)
0 152481
44.2%
1 70891
20.6%
2 41259
 
12.0%
3 25274
 
7.3%
4 16223
 
4.7%
5 10931
 
3.2%
6 7532
 
2.2%
7 5314
 
1.5%
8 3801
 
1.1%
9 2790
 
0.8%
ValueCountFrequency (%)
74 1
< 0.1%
73 1
< 0.1%
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%

AmountOfPreviousLoansBeforeLoan
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21416
Distinct (%)6.2%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3691.0335
Minimum0
Maximum119983
Zeros152475
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:40.042545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1036
Q35365
95-th percentile14445.75
Maximum119983
Range119983
Interquartile range (IQR)5365

Descriptive statistics

Standard deviation5552.4591
Coefficient of variation (CV)1.5043102
Kurtosis12.702169
Mean3691.0335
Median Absolute Deviation (MAD)1036
Skewness2.5451427
Sum1.272912 × 109
Variance30829802
MonotonicityNot monotonic
2023-12-10T13:10:40.250055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 152475
44.2%
4150 8140
 
2.4%
518 4679
 
1.4%
531 3727
 
1.1%
530 3164
 
0.9%
8300 3043
 
0.9%
2125 2984
 
0.9%
4146 2898
 
0.8%
4250 2669
 
0.8%
2126 2498
 
0.7%
Other values (21406) 158589
46.0%
ValueCountFrequency (%)
0 152475
44.2%
12.7862 1
 
< 0.1%
25.5647 1
 
< 0.1%
25.5651 1
 
< 0.1%
31.9443 1
 
< 0.1%
31.9489 1
 
< 0.1%
31.95 1
 
< 0.1%
31.9521 1
 
< 0.1%
31.955 1
 
< 0.1%
31.9557 1
 
< 0.1%
ValueCountFrequency (%)
119983 1
< 0.1%
116368 1
< 0.1%
108926 1
< 0.1%
101484 1
< 0.1%
101073 1
< 0.1%
90852 1
< 0.1%
90441 1
< 0.1%
88121 1
< 0.1%
87590 1
< 0.1%
84294 1
< 0.1%

PreviousRepaymentsBeforeLoan
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct100808
Distinct (%)47.3%
Missing131766
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean1170.0567
Minimum0
Maximum34077.42
Zeros30663
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2023-12-10T13:10:40.504299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1128.24
median492.955
Q31276.96
95-th percentile4711.2835
Maximum34077.42
Range34077.42
Interquartile range (IQR)1148.72

Descriptive statistics

Standard deviation1926.1366
Coefficient of variation (CV)1.6461908
Kurtosis20.972193
Mean1170.0567
Median Absolute Deviation (MAD)431.855
Skewness3.6682804
Sum2.4935313 × 108
Variance3710002.1
MonotonicityNot monotonic
2023-12-10T13:10:40.676373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30663
 
8.9%
520.77 91
 
< 0.1%
519.39 70
 
< 0.1%
724.48 68
 
< 0.1%
520.22 57
 
< 0.1%
518.55 55
 
< 0.1%
519.94 51
 
< 0.1%
4153.46 46
 
< 0.1%
518.43 44
 
< 0.1%
519.11 44
 
< 0.1%
Other values (100798) 181923
52.7%
(Missing) 131766
38.2%
ValueCountFrequency (%)
0 30663
8.9%
1.5 1
 
< 0.1%
1.5677 1
 
< 0.1%
1.69 1
 
< 0.1%
1.94 2
 
< 0.1%
2.06 1
 
< 0.1%
2.08 1
 
< 0.1%
2.27 1
 
< 0.1%
2.28 1
 
< 0.1%
2.46 1
 
< 0.1%
ValueCountFrequency (%)
34077.42 1
< 0.1%
33874.18 1
< 0.1%
33558.09 1
< 0.1%
33135.66 1
< 0.1%
31098.85 1
< 0.1%
29664.89 1
< 0.1%
29535.43 1
< 0.1%
28621.95 1
< 0.1%
27921.69 1
< 0.1%
27822.14 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size336.9 KiB
False
284488 
True
60390 
ValueCountFrequency (%)
False 284488
82.5%
True 60390
 
17.5%
2023-12-10T13:10:40.830753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Interactions

2023-12-10T13:10:24.334132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:08.495746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:18.762819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:25.792525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:31.824123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:36.069793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:40.736334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:45.625176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-12-10T13:10:20.047016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-12-10T13:10:26.811661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:17.147671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:24.388077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:29.848428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:35.271828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:39.969382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:44.628847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:49.333261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:54.621585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:58.288344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:03.015314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:06.494894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:10.818096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:15.631488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:20.245856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:23.718120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:27.062167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:17.984824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:24.862420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:30.065439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:35.520078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:40.165155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:44.892424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:49.553993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:54.844554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:58.486080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:03.295361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:06.706878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:11.021048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:15.899936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:20.429209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:23.921086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:27.233503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:18.326785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:25.249535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:30.251329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:35.730652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:40.361570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:45.235626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:49.780079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:55.046782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:09:58.668742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:03.558759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:06.915160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:11.187501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:16.084607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:20.600540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-12-10T13:10:24.090087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-12-10T13:10:40.954332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ApplicationSignedWeekdayLanguageCodeAgeAppliedAmountUseOfLoanEducationMaritalStatusEmploymentStatusOccupationAreaHomeOwnershipTypeIncomeTotalExistingLiabilitiesLiabilitiesTotalNoOfPreviousLoansBeforeLoanAmountOfPreviousLoansBeforeLoanPreviousRepaymentsBeforeLoanNewCreditCustomerGenderCountryEmploymentDurationCurrentEmployerDefaultWithin12Months
ApplicationSignedWeekday1.0000.0090.003-0.008-0.009-0.006-0.009-0.009-0.009-0.0020.005-0.008-0.008-0.003-0.0020.0030.0270.0130.0190.0190.027
LanguageCode0.0091.0000.1360.086-0.059-0.051-0.059-0.059-0.0610.0710.442-0.1870.013-0.313-0.278-0.0860.2780.5090.9930.0910.296
Age0.0030.1361.0000.113-0.0720.077-0.072-0.066-0.070-0.1260.1110.0840.1380.0570.0940.1070.0920.0520.0940.2330.057
AppliedAmount-0.0080.0860.1131.000-0.003-0.003-0.0000.0030.002-0.0100.172-0.0840.028-0.155-0.0760.0420.1340.1380.2200.0530.113
UseOfLoan-0.009-0.059-0.072-0.0031.0000.0780.9960.9970.9950.032-0.1390.2310.315-0.155-0.170-0.3660.0080.0030.0131.0000.008
Education-0.006-0.0510.077-0.0030.0781.0000.0780.0790.082-0.0320.0310.1350.1490.0840.0810.0080.1340.1110.4430.1290.079
MaritalStatus-0.009-0.059-0.072-0.0000.9960.0781.0000.9970.9950.032-0.1400.2350.317-0.157-0.172-0.3700.0960.0580.1280.2320.124
EmploymentStatus-0.009-0.059-0.0660.0030.9970.0790.9971.0000.9960.032-0.1390.2370.320-0.156-0.171-0.3680.0960.0360.1210.2110.116
OccupationArea-0.009-0.061-0.0700.0020.9950.0820.9950.9961.0000.032-0.1370.2370.318-0.155-0.170-0.3640.0750.0660.1130.1610.093
HomeOwnershipType-0.0020.071-0.126-0.0100.032-0.0320.0320.0320.0321.0000.012-0.041-0.049-0.086-0.088-0.0690.1200.1240.1900.1360.124
IncomeTotal0.0050.4420.1110.172-0.1390.031-0.140-0.139-0.1370.0121.000-0.0330.190-0.0580.0020.1970.0040.0030.0060.0050.006
ExistingLiabilities-0.008-0.1870.084-0.0840.2310.1350.2350.2370.237-0.041-0.0331.0000.7920.5240.4960.0620.2770.0350.0830.0630.029
LiabilitiesTotal-0.0080.0130.1380.0280.3150.1490.3170.3200.318-0.0490.1900.7921.0000.2500.2930.0490.0000.0070.0040.0000.000
NoOfPreviousLoansBeforeLoan-0.003-0.3130.057-0.155-0.1550.084-0.157-0.156-0.155-0.086-0.0580.5240.2501.0000.9480.5780.2250.0350.0860.0410.062
AmountOfPreviousLoansBeforeLoan-0.002-0.2780.094-0.076-0.1700.081-0.172-0.171-0.170-0.0880.0020.4960.2930.9481.0000.7070.3260.0460.0840.0400.082
PreviousRepaymentsBeforeLoan0.003-0.0860.1070.042-0.3660.008-0.370-0.368-0.364-0.0690.1970.0620.0490.5780.7071.0000.1840.0360.0470.0280.073
NewCreditCustomer0.0270.2780.0920.1340.0080.1340.0960.0960.0750.1200.0040.2770.0000.2250.3260.1841.0000.1260.2770.1010.116
Gender0.0130.5090.0520.1380.0030.1110.0580.0360.0660.1240.0030.0350.0070.0350.0460.0360.1261.0000.5100.0590.215
Country0.0190.9930.0940.2200.0130.4430.1280.1210.1130.1900.0060.0830.0040.0860.0840.0470.2770.5101.0000.1220.297
EmploymentDurationCurrentEmployer0.0190.0910.2330.0531.0000.1290.2320.2110.1610.1360.0050.0630.0000.0410.0400.0280.1010.0590.1221.0000.083
DefaultWithin12Months0.0270.2960.0570.1130.0080.0790.1240.1160.0930.1240.0060.0290.0000.0620.0820.0730.1160.2150.2970.0831.000

Missing values

2023-12-10T13:10:27.864353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T13:10:29.106622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T13:10:31.798281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NewCreditCustomerApplicationSignedWeekdayLanguageCodeAgeGenderCountryAppliedAmountUseOfLoanEducationMaritalStatusEmploymentStatusEmploymentDurationCurrentEmployerOccupationAreaHomeOwnershipTypeIncomeTotalExistingLiabilitiesLiabilitiesTotalNoOfPreviousLoansBeforeLoanAmountOfPreviousLoansBeforeLoanPreviousRepaymentsBeforeLoanDefaultWithin12Months
0False61271.0EE3189.0-13.0-1.0-1.0UpTo5Years-1.010.0900.000.001.03402.01161.57False
1False44351.0FI4146.0-15.0-1.0-1.0UpTo5Years-1.01.03100.000.001.0518.0525.21False
2False41531.0EE2125.024.02.06.0MoreThan5Years1.01.0354.08485.091.0500.0590.95False
3False34430.0FI414.0-13.0-1.0-1.0Other-1.03.01200.000.005.011198.01176.22False
4False51250.0EE531.0-11.0-1.0-1.0UpTo5Years-1.01.0947.000.008.08609.0931.98False
5False71391.0EE425.0-14.0-1.0-1.0MoreThan5Years-1.01.01500.000.005.011268.01378.09False
6False54401.0FI518.0-15.0-1.0-1.0MoreThan5Years-1.01.04800.000.0010.019283.02141.75False
7False51501.0EE3000.035.02.05.0MoreThan5Years7.01.0900.04736.451.01800.0445.26True
8False34500.0FI518.0-15.0-1.0-1.0Other-1.03.01275.000.007.05076.0194.69False
9True31440.0EE10630.034.04.05.0UpTo3Years8.08.01200.07905.000.00.00.00False
NewCreditCustomerApplicationSignedWeekdayLanguageCodeAgeGenderCountryAppliedAmountUseOfLoanEducationMaritalStatusEmploymentStatusEmploymentDurationCurrentEmployerOccupationAreaHomeOwnershipTypeIncomeTotalExistingLiabilitiesLiabilitiesTotalNoOfPreviousLoansBeforeLoanAmountOfPreviousLoansBeforeLoanPreviousRepaymentsBeforeLoanDefaultWithin12Months
344868False21640.0EE1300.064.01.04.0MoreThan5Years19.01.0867.08804.24.08000.04462.13False
344869False51521.0EE531.0-11.0-1.0-1.0MoreThan5Years-1.01.0400.000.01.01594.01291.73False
344870True14370.0FI4146.0-14.0-1.0-1.0UpTo5Years-1.03.02600.000.01.04146.0NaNFalse
344871False11340.0EE850.0-13.0-1.0-1.0MoreThan5Years-1.01.01700.000.06.014457.06187.04False
344872False63381.0EE531.0-14.0-1.0-1.0MoreThan5Years-1.01.0900.000.05.06373.0592.95False
344873False63561.0EE318.0-14.0-1.0-1.0Other-1.01.0500.000.05.010630.0190.65False
344874False23610.0EE4253.0-15.0-1.0-1.0Retiree-1.01.0550.000.09.011159.02436.35False
344875False23241.0EE531.0-11.0-1.0-1.0UpTo5Years-1.01.0800.000.06.03825.0112.06False
344876False24441.0FI518.0-13.0-1.0-1.0UpTo5Years-1.03.01300.000.05.06222.0972.44False
344877False41491.0EE425.0-15.0-1.0-1.0MoreThan5Years-1.01.01600.000.010.012009.01045.86False

Duplicate rows

Most frequently occurring

NewCreditCustomerApplicationSignedWeekdayLanguageCodeAgeGenderCountryAppliedAmountUseOfLoanEducationMaritalStatusEmploymentStatusEmploymentDurationCurrentEmployerOccupationAreaHomeOwnershipTypeIncomeTotalExistingLiabilitiesLiabilitiesTotalNoOfPreviousLoansBeforeLoanAmountOfPreviousLoansBeforeLoanPreviousRepaymentsBeforeLoanDefaultWithin12Months# duplicates
58True41191.0EE531.0-11.0-1.0-1.0UpTo1Year-1.03.0800.000.00.00.0NaNFalse3
89True56300.0ES530.0-14.0-1.0-1.0MoreThan5Years-1.010.01100.000.00.00.00.0True3
0True11191.0EE531.0-11.0-1.0-1.0Other-1.02.0400.000.00.00.0NaNFalse2
1True14310.0FI518.0-13.0-1.0-1.0UpTo5Years-1.03.02200.000.00.00.0NaNFalse2
2True14321.0FI518.0-13.0-1.0-1.0UpTo1Year-1.03.02000.000.00.00.0NaNFalse2
3True14331.0FI518.0-13.0-1.0-1.0UpTo5Years-1.03.02100.000.00.00.0NaNFalse2
4True21200.0EE530.0-14.0-1.0-1.0UpTo1Year-1.02.01000.000.00.00.0NaNFalse2
5True21200.0EE530.0-14.0-1.0-1.0UpTo5Years-1.02.0800.000.00.00.0NaNFalse2
6True22310.0EE2126.0-15.0-1.0-1.0UpTo5Years-1.03.01000.000.00.00.0NaNFalse2
7True23180.0EE531.0-11.0-1.0-1.0UpTo1Year-1.02.0800.000.00.00.0NaNFalse2